Browsing by Author "Gerhard Hancke"

Now showing 1 - 4 of 4

A survey of cognitive radio handoff schemes, challenges and issues for industrial wireless sensor networks
(Elsevier, Journal of Network and Computer Applications, 2017-08-25) Oyewobi S. Stephen; Gerhard Hancke
Industrial wireless sensor network (IWSN) applications are mostly time-bound, mission-critical and highly delay sensitive applications therefore IWSN defines strict, stringent and unique QoS requirements such as timeliness, reliability and availability. In IWSN, unlike other sensor networks, late arrival of packets or delay or disruption to an on-going communication are considered as critical failure. Also, because IWSN is deployed in the overcrowded industrial, scientific, and medical (ISM) band it is difficult to meet this unique QoS requirements due to stiff competition for bandwidth from other technologies operating in ISM band resulting in scarcity of spectrum for reliable communication and/or disruption of ongoing communication. However, cognitive radio (CR) provides more spectral opportunities through opportunistic-use of unused licensed spectrum while ensuring minimal interference to licensed users. Similarly, spectrum handoff, which is a new type of handoff in cognitive radio, has the potential to offer increase bandwidth, reliable, smooth and interference-free communication for IWSNs through opportunistic-use of spectrum, minimal switching-delays, and efficient target channel selection strategies as well as effective link recovery maintenance. As a result, a new paradigm known as cognitive radio industrial wireless sensor network (CR-IWSN) has become the interest of recent research efforts. In this paper, we highlight and discuss important QoS requirements of IWSN as well as efforts of existing IWSN standards to address the challenges. We discuss the potential and how cognitive radio and spectrum handoff can be useful in the attempt to provide real-time reliable and smooth communication for IWSNs.
An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things
(MDPI Sensors, 2019-03-21) Oyewobi S. Stephen; Gerhard Hancke; Adnan M. Abu-Mahfouz; Adeiza J. Onumanyi
The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches
Enabling a Battery-Less Sensor Node Using Dedicated Radio Frequency Energy Harvesting for Complete Off-Grid Applications
(MDPI, 2020-10-16) Timothy Miller; Oyewobi S. Stephen; Adnan M. Abu-Mahfouz; Gerhard Hancke
The large-scale deployment of sensor nodes in difficult-to-reach locations makes powering of sensor nodes via batteries impractical. Besides, battery-powered WSNs require the periodic replacement of batteries. Wireless, battery-less sensor nodes represent a less maintenance-intensive, more environmentally friendly and compact alternative to battery powered sensor nodes. Moreover, such nodes are powered through wireless energy harvesting. In this research, we propose a novel battery-less wireless sensor node which is powered by a dedicated 4 W EIRP 920 MHz radio frequency (RF) energy device. The system is designed to provide complete off-grid Internet of Things (IoT) applications. To this end we have designed a power base station which derives its power from solar PV panels to radiate the RF energy used to power the sensor node. We use a PIC32MX220F32 microcontroller to implement a CC-CV battery charging algorithm to control the step-down DC-DC converter which charges lithium-ion batteries that power the RF transmitter and amplifier, respectively. A 12 element Yagi antenna was designed and optimized using the FEKO electromagnetic software. We design a step-up converter to step the voltage output from a single stage fully cross-coupled RF-DC converter circuit up to 3.3 V. Finally, we use the power requirements of the sensor node to size the storage capacity of the capacitor of the energy harvesting circuit. The results obtained from the experiments performed showed that enough RF energy was harvested over a distance of 15 m to allow the sensor node complete one sense-transmit operation for a duration of 156 min. The Yagi antenna achieved a gain of 12.62 dBi and a return loss of −14.11 dB at 920 MHz, while the battery was correctly charged according to the CC-CV algorithm through the control of the DC-DC converter.
IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution
(IEEEE Access, 2020) Musa Ndiaye; Oyewobi S. Stephen; Adnan M. Abu-Mahfouz; Gerhard Hancke; Anish M. Kurien; Karim Djouani
The novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to the general way of life. Major sectors of the world industry and economy have been affected and the Internet of Things (IoT) management and framework is no exception in this regard. This article provides an up to date survey on how a global pandemic such as COVID-19 has affected the world of IoT technologies. It looks at the contributions that IoT and associated sensor technologies have made towards virus tracing, tracking and spread mitigation. The associated challenges of deployment of sensor hardware in the face of a rapidly spreading pandemic have been looked into as part of this review article. The effects of a global pandemic on the evolution of IoT architectures and management have also been addressed, leading to the likely outcomes on future IoT implementations. In general, this article provides an insight into the advancement of sensor-based E-health towards the management of global pandemics. It also answers the question of how a global virus pandemic has shaped the future of IoT networks.